A CoMP system was designed to apply advanced Multiple Input Multiple Output (MIMO) to a multi-cell environment, with the goal of improving the throughput of users at a cell edge. The use of the CoMP system may reduce inter-cell interference in the multi-cell environment. In this CoMP system, multi-cell Base Stations (BSs) may support joint data transmission to a User Equipment (UE).
Each BS may support one or more UEs, UE 1 to UE K simultaneously using the same radio frequency resources, thus increasing system performance. Also, the BS may operate in Space Division Multiple Access (SDMA) based on Channel State Information (CSI) between the BS and the UEs.
There are two types of CoMP schemes: Joint Processing (JP) based on data sharing and Coordinated Scheduling/Beamforming (CS/CB).
FIG. 1 illustrates the concept of intra-evolved Node B (intra-eNB) CoMP and inter-eNB CoMP.
Referring to FIG. 1, there are intra-eNBs 110 and 120 and an inter-eNB 130 in a multi-cell environment. In Long Term Evolution (LTE), an intra-eNB covers a plurality of cells or sectors. Cells covered by an eNB to which a UE belongs to are in an intra-eNB relationship with the UE. In other words, cells under the same eNB managing the cell of a UE are intra-eNB cells, whereas cells under a different eNB from the eNB of the UE are inter-eNB cells. Cells within the same eNB exchange information (e.g. data and CSI) with each other via ×2 interfaces or the like. Cells in different eNBs exchange information through a backhaul 140.
As illustrated in FIG. 1, a single-cell MIMO user 150 in a single cell or sector may communicate with a serving eNB in the cell or sector, and a multi-cell MIMO user 160 at a cell edge may communicate with a plurality of serving eNBs in multiple cells or sectors.
A brief description will be made of a spatial channel matrix that may be used as feedback information.
A spatial channel matrix H(i,k) may be given as
      H    ⁡          (              i        ,        k            )        =      [                                                      h                              1                ,                1                                      ⁡                          (                              i                ,                k                            )                                                                          h                              2                ,                1                                      ⁡                          (                              i                ,                k                            )                                                …                                                    h                              1                ,                Nt                                      ⁡                          (                              i                ,                k                            )                                                                                      h                              2                ,                1                                      ⁡                          (                              i                ,                k                            )                                                                          h                              2                ,                2                                      ⁡                          (                              i                ,                k                            )                                                …                                                    h                              2                ,                Nt                                      ⁡                          (                              i                ,                k                            )                                                            ⋮                          ⋮                          ⋱                          ⋮                                                                h                              Nr                ,                1                                      ⁡                          (                              i                ,                k                            )                                                                          h                              Nr                ,                2                                      ⁡                          (                              i                ,                k                            )                                                …                                                    h                              Nr                ,                Nt                                      ⁡                          (                              i                ,                k                            )                                            ]  where hr,t(i,k) denotes an element of the spatial channel matrix H(i,k), Nr denotes the number of Reception (Rx) antennas, Nt denotes the number of Transmission (Tx) antennas, r denotes the index of an Rx antenna, t denotes the index of a Tx antenna, i denotes the index of an Orthogonal Frequency Division Multiplexing (OFDM) or Single Carrier-Orthogonal Frequency Division Multiplexing (SC-OFDM) symbol, and k denotes the index of a subcarrier.
An element of the spatial channel matrix H(i,k) hr,t(i,k) represents the channel status between an rth Rx antenna and a tth Tx antenna.
A spatial channel covariance matrix R that is applicable to the present invention may be expressed as R=E[Hi,kHi,kH] where H denotes the spatial channel matrix, E[ ] denotes a mean, i denotes a symbol index, and k denotes a subcarrier index.
Singular Value Decomposition (SVD) is one of significant factorizations of a rectangular matrix, with many applications in signal processing and statistics. The SVD is a generalization of the spectral theorem of matrices to arbitrary rectangular matrices. The spectral theorem says that an orthogonal square matrix can be unitarily diagonalized using a base of eigenvalues. Let the channel matrix H be an m×m matrix having real or complex entries. Then the channel matrix H may be expressed as the product of the following three matrices.Hm×m=Um×mΣm×nVm×nH where U and V are unitary matrices and Σ is an m×n diagonal matrix with non-negative singular values. For the singular values, Σ=diag(σ1 . . . σr), σi=√{square root over (λi)}. This factorization into the product of three matrices is called SVD. The SVD is very general in the sense that it can be applied to any matrices whereas EigenValue Decomposition (EVD) can be applied only to orthogonal square matrices. Nevertheless, the two decompositions are related.
If the channel matrix H is a positive, definite Hermitian matrix, all eigenvalues of the channel matrix H are non-negative real numbers. The singular values and singular vectors of the channel matrix H are its eigenvalues and eigenvectors.
The EVD may be expressed asHHH=(UΣVH)(UΣVH)H=UΣΣTUH HHH=(UΣVH)H(UΣVH)H=VΣTΣV where the eigenvalues may be λ1 . . . λr.
Conventionally, MSs transmit only Precoding Matrix Indexes (PMIs) as feedback information to an eNB, thus making it difficult for the eNB to prioritize the PMIs. Consequently, a CoMP operation suffers from decreased efficiency and accuracy due to the difficulty in ranking PMIs.